"""simple docstring"""



from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__:Tuple									=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:Dict									=			{
    """microsoft/xprophetnet-large-wiki100-cased""": (
        """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"""
    ),
}






class  snake_case__      (       snake_case_       ):
 _snake_case       :					Any							  =						"""xlm-prophetnet"""
 _snake_case       :					Optional[Any]							  =						["""past_key_values"""]
 _snake_case       :					Any							  =						{
     """num_attention_heads""": """num_encoder_attention_heads""",
 }


 def __init__( self    ,		lowerCamelCase = 0.1    ,		lowerCamelCase = "gelu"    ,		lowerCamelCase = 30522    ,		lowerCamelCase = 1024    ,		lowerCamelCase = 4096    ,		lowerCamelCase = 12    ,		lowerCamelCase = 16    ,		lowerCamelCase = 4096    ,		lowerCamelCase = 12    ,		lowerCamelCase = 16    ,		lowerCamelCase = 0.1    ,		lowerCamelCase = 0.1    ,		lowerCamelCase = 512    ,		lowerCamelCase = 0.02    ,		lowerCamelCase = True    ,		lowerCamelCase = True    ,		lowerCamelCase = 0    ,		lowerCamelCase = 2    ,		lowerCamelCase = 32    ,		lowerCamelCase = 128    ,		lowerCamelCase = False    ,		lowerCamelCase = 0.0    ,		lowerCamelCase = True    ,		lowerCamelCase = 0    ,		lowerCamelCase = 1    ,		lowerCamelCase = 2    ,		**lowerCamelCase    ,		):
        __a       =    vocab_size
        __a       =    hidden_size
        __a       =    encoder_ffn_dim
        __a       =    num_encoder_layers
        __a       =    num_encoder_attention_heads
        __a       =    decoder_ffn_dim
        __a       =    num_decoder_layers
        __a       =    num_decoder_attention_heads
        __a       =    max_position_embeddings
        __a       =    init_std  # Normal(0, this parameter)
        __a       =    activation_function

        # parameters for xlmprophetnet
        __a       =    ngram
        __a       =    num_buckets
        __a       =    relative_max_distance
        __a       =    disable_ngram_loss
        __a       =    eps

        # 3 Types of Dropout
        __a       =    attention_dropout
        __a       =    activation_dropout
        __a       =    dropout

        __a       =    use_cache

        super().__init__(
            pad_token_id=lowerCamelCase    ,		bos_token_id=lowerCamelCase    ,		eos_token_id=lowerCamelCase    ,		is_encoder_decoder=lowerCamelCase    ,		add_cross_attention=lowerCamelCase    ,		decoder_start_token_id=lowerCamelCase    ,		**lowerCamelCase    ,		)


 @property
 def 						a__   ( self						):
        return self.num_encoder_layers + self.num_decoder_layers






 @num_hidden_layers.setter
 def 						a__   ( self    ,		lowerCamelCase						):
        raise NotImplementedError(
            "This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"
            " `num_decoder_layers`."						)

"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__:Optional[Any]									=			{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:Optional[Any]									=			["""ReformerTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:List[str]									=			["""ReformerTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:int									=			[
       """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """ReformerAttention""",
       """ReformerForMaskedLM""",
       """ReformerForQuestionAnswering""",
       """ReformerForSequenceClassification""",
       """ReformerLayer""",
       """ReformerModel""",
       """ReformerModelWithLMHead""",
       """ReformerPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer import ReformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer_fast import ReformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_reformer import (
          REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          ReformerAttention,
          ReformerForMaskedLM,
          ReformerForQuestionAnswering,
          ReformerForSequenceClassification,
          ReformerLayer,
          ReformerModel,
          ReformerModelWithLMHead,
          ReformerPreTrainedModel,
      )

else:
   import sys

   SCREAMING_SNAKE_CASE__:int									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
